{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习工程师纳米学位\n",
    "## 模型评价与验证\n",
    "## 项目 1: 预测波士顿房价\n",
    "\n",
    "\n",
    "欢迎来到机器学习的预测波士顿房价项目！在此文件中，有些示例代码已经提供给你，但你还需要实现更多的功能来让项目成功运行。除非有明确要求，你无须修改任何已给出的代码。以**编程练习**开始的标题表示接下来的内容中有需要你必须实现的功能。每一部分都会有详细的指导，需要实现的部分也会在注释中以**TODO**标出。请仔细阅读所有的提示！\n",
    "\n",
    "除了实现代码外，你还**必须**回答一些与项目和实现有关的问题。每一个需要你回答的问题都会以**'问题 X'**为标题。请仔细阅读每个问题，并且在问题后的**'回答'**文字框中写出完整的答案。你的项目将会根据你对问题的回答和撰写代码所实现的功能来进行评分。\n",
    "\n",
    ">**提示：**Code 和 Markdown 区域可通过 **Shift + Enter** 快捷键运行。此外，Markdown可以通过双击进入编辑模式。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第一步. 导入数据\n",
    "在这个项目中，你将利用马萨诸塞州波士顿郊区的房屋信息数据训练和测试一个模型，并对模型的性能和预测能力进行测试。通过该数据训练后的好的模型可以被用来对房屋做特定预测---尤其是对房屋的价值。对于房地产经纪等人的日常工作来说，这样的预测模型被证明非常有价值。\n",
    "\n",
    "此项目的数据集来自[UCI机器学习知识库(数据集已下线)](https://archive.ics.uci.edu/ml/datasets.html)。波士顿房屋这些数据于1978年开始统计，共506个数据点，涵盖了麻省波士顿不同郊区房屋14种特征的信息。本项目对原始数据集做了以下处理：\n",
    "- 有16个`'MEDV'` 值为50.0的数据点被移除。 这很可能是由于这些数据点包含**遗失**或**看不到的值**。\n",
    "- 有1个数据点的 `'RM'` 值为8.78. 这是一个异常值，已经被移除。\n",
    "- 对于本项目，房屋的`'RM'`， `'LSTAT'`，`'PTRATIO'`以及`'MEDV'`特征是必要的，其余不相关特征已经被移除。\n",
    "- `'MEDV'`特征的值已经过必要的数学转换，可以反映35年来市场的通货膨胀效应。\n",
    "\n",
    "运行下面区域的代码以载入波士顿房屋数据集，以及一些此项目所需的 Python 库。如果成功返回数据集的大小，表示数据集已载入成功。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston housing dataset has 489 data points with 4 variables each.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/learning_curve.py:22: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the functions are moved. This module will be removed in 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "# Import supplementary visualizations code visuals.py\n",
    "import visuals as vs\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the Boston housing dataset\n",
    "data = pd.read_csv('housing.csv')\n",
    "prices = data['MEDV']\n",
    "features = data.drop('MEDV', axis = 1)\n",
    "    \n",
    "# Success\n",
    "print(\"Boston housing dataset has {} data points with {} variables each.\".format(*data.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第二步. 分析数据\n",
    "在项目的第一个部分，你会对波士顿房地产数据进行初步的观察并给出你的分析。通过对数据的探索来熟悉数据可以让你更好地理解和解释你的结果。\n",
    "\n",
    "由于这个项目的最终目标是建立一个预测房屋价值的模型，我们需要将数据集分为**特征(features)**和**目标变量(target variable)**。\n",
    "- **特征** `'RM'`， `'LSTAT'`，和 `'PTRATIO'`，给我们提供了每个数据点的数量相关的信息。\n",
    "- **目标变量**：` 'MEDV'`，是我们希望预测的变量。\n",
    "\n",
    "他们分别被存在 `features` 和 `prices` 两个变量名中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 1：基础统计运算\n",
    "你的第一个编程练习是计算有关波士顿房价的描述统计数据。我们已为你导入了 ` NumPy `，你需要使用这个库来执行必要的计算。这些统计数据对于分析模型的预测结果非常重要的。\n",
    "在下面的代码中，你要做的是：\n",
    "- 计算 `prices` 中的 `'MEDV'` 的最小值、最大值、均值、中值和标准差；\n",
    "- 将运算结果储存在相应的变量中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for Boston housing dataset:\n",
      "\n",
      "Minimum price: $105000.00\n",
      "Maximum price: $1024800.00\n",
      "Mean price: $454342.94\n",
      "Median price $438900.00\n",
      "Standard deviation of prices: $165171.13\n"
     ]
    }
   ],
   "source": [
    "# TODO: Minimum price of the data\n",
    "minimum_price = np.min(prices)\n",
    "\n",
    "# TODO: Maximum price of the data\n",
    "maximum_price = np.max(prices)\n",
    "\n",
    "# TODO: Mean price of the data\n",
    "mean_price = np.mean(prices)\n",
    "\n",
    "# TODO: Median price of the data\n",
    "median_price = np.median(prices)\n",
    "\n",
    "# TODO: Standard deviation of prices of the data\n",
    "std_price = np.std(prices)\n",
    "\n",
    "# Show the calculated statistics\n",
    "print(\"Statistics for Boston housing dataset:\\n\")\n",
    "print(\"Minimum price: ${:.2f}\".format(minimum_price)) \n",
    "print(\"Maximum price: ${:.2f}\".format(maximum_price))\n",
    "print(\"Mean price: ${:.2f}\".format(mean_price))\n",
    "print(\"Median price ${:.2f}\".format(median_price))\n",
    "print(\"Standard deviation of prices: ${:.2f}\".format(std_price))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 1 - 特征观察\n",
    "\n",
    "如前文所述，本项目中我们关注的是其中三个值:`'RM'`、`'LSTAT'` 和`'PTRATIO'`，对每一个数据点:\n",
    "- `'RM'` 是该地区中每个房屋的平均房间数量；\n",
    "- `'LSTAT'` 是指该地区有多少百分比的业主属于是低收入阶层（有工作但收入微薄）；\n",
    "- `'PTRATIO'` 是该地区的中学和小学里，学生和老师的数目比（`学生/老师`）。\n",
    "\n",
    "_凭直觉，上述三个特征中对每一个来说，你认为增大该特征的数值，`'MEDV'`的值会是**增大**还是**减小**呢？每一个答案都需要你给出理由。_\n",
    "\n",
    "**提示：**你预期一个`'RM'` 值是6的房屋跟`'RM'` 值是7的房屋相比，价值更高还是更低呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f212590a438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "dataset = ['RM', 'LSTAT', 'PTRATIO']\n",
    "# 设定画图板尺寸\n",
    "plt.figure(figsize=(15, 3.5))\n",
    "for i, key in enumerate(dataset):\n",
    "    # 设定子图，将每个子图输出到对应的位置,1行3列i+1依次排列\n",
    "    plt.subplot(131+i)\n",
    "    plt.title(key + ' - MEDV')\n",
    "    plt.xlabel('factor( ' + key + ' )')\n",
    "    plt.ylabel('MEDV')\n",
    "    # 调整每隔子图之间的距离\n",
    "    plt.tight_layout()\n",
    "    plt.scatter(data[key], data['MEDV'], alpha = 0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 1 - 回答："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 图一 RM 与 MEDV 成正相关，RM 越多，MEDV 越高\n",
    "- 图二 LSTAT 与 MEDV 成负相关，LSTAT 越多，MEDV 越低\n",
    "- 图三 PTRATIO 与 MEDV 相关性不明显，PTRATIO 比例越大，MEDV 在部分区间内下降"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第三步. 建立模型\n",
    "在项目的第三步中，你需要了解必要的工具和技巧来让你的模型进行预测。用这些工具和技巧对每一个模型的表现做精确的衡量可以极大地增强你预测的信心。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习2：定义衡量标准\n",
    "如果不能对模型的训练和测试的表现进行量化地评估，我们就很难衡量模型的好坏。通常我们会定义一些衡量标准，这些标准可以通过对某些误差或者拟合程度的计算来得到。在这个项目中，你将通过运算[决定系数](https://en.wikipedia.org/wiki/Coefficient_of_determination) $R^2$ 来量化模型的表现。模型的决定系数是回归分析中十分常用的统计信息，经常被当作衡量模型预测能力好坏的标准。\n",
    "\n",
    "$R^2$ 的数值范围从0至1，表示**目标变量**的预测值和实际值之间的相关程度平方的百分比。一个模型的 $R^2$ 值为0还不如直接用**平均值**来预测效果好；而一个 $R^2$ 值为1的模型则可以对目标变量进行完美的预测。从0至1之间的数值，则表示该模型中目标变量中有百分之多少能够用**特征**来解释。模型也可能出现负值的 $R^2$，这种情况下模型所做预测有时会比直接计算目标变量的平均值差很多。\n",
    "\n",
    "在下方代码的 `performance_metric` 函数中，你要实现：\n",
    "- 使用 `sklearn.metrics` 中的 [`r2_score`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html) 来计算 `y_true` 和 `y_predict` 的 $R^2$ 值，作为对其表现的评判。\n",
    "- 将他们的表现评分储存到 `score` 变量中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Import 'r2_score'\n",
    "from  sklearn.metrics import r2_score\n",
    "\n",
    "def performance_metric(y_true, y_predict):\n",
    "    \"\"\" Calculates and returns the performance score between \n",
    "        true and predicted values based on the metric chosen. \"\"\"\n",
    "    \n",
    "    # TODO: Calculate the performance score between 'y_true' and 'y_predict'\n",
    "    score = r2_score(y_true, y_predict)\n",
    "    \n",
    "    # Return the score\n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 2 - 拟合程度\n",
    "\n",
    "假设一个数据集有五个数据且一个模型做出下列目标变量的预测：\n",
    "\n",
    "| 真实数值 | 预测数值 |\n",
    "| :-------------: | :--------: |\n",
    "| 3.0 | 2.5 |\n",
    "| -0.5 | 0.0 |\n",
    "| 2.0 | 2.1 |\n",
    "| 7.0 | 7.8 |\n",
    "| 4.2 | 5.3 |\n",
    "*你觉得这个模型已成功地描述了目标变量的变化吗？如果成功，请解释为什么，如果没有，也请给出原因。*  \n",
    "\n",
    "**提示1**：运行下方的代码，使用 `performance_metric` 函数来计算 `y_true` 和 `y_predict` 的决定系数。\n",
    "\n",
    "**提示2**：$R^2$ 分数是指可以从自变量中预测的因变量的方差比例。 换一种说法：\n",
    "\n",
    "* $R^2$ 为0意味着因变量不能从自变量预测。\n",
    "* $R^2$ 为1意味着可以从自变量预测因变量。\n",
    "* $R^2$ 在0到1之间表示因变量可预测的程度。\n",
    "* $R^2$ 为0.40意味着 Y 中40％的方差可以从 X 预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model has a coefficient of determination, R^2, of 0.923.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the performance of this model\n",
    "score = performance_metric([3, -0.5, 2, 7, 4.2], [2.5, 0.0, 2.1, 7.8, 5.3])\n",
    "print(\"Model has a coefficient of determination, R^2, of {:.3f}.\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 2 - 回答:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$R^2$ = 0.923 接近于 1 ，说明模型成功地描述了目标变量的变化。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 3: 数据分割与重排\n",
    "接下来，你需要把波士顿房屋数据集分成训练和测试两个子集。通常在这个过程中，数据也会被重排列，以消除数据集中由于顺序而产生的偏差。\n",
    "在下面的代码中，你需要\n",
    "\n",
    "* 使用 `sklearn.model_selection` 中的 `train_test_split`， 将 `features` 和 `prices` 的数据都分成用于训练的数据子集和用于测试的数据子集。\n",
    "  - 分割比例为：80%的数据用于训练，20%用于测试；\n",
    "  - 选定一个数值以设定 `train_test_split` 中的 `random_state` ，这会确保结果的一致性；\n",
    "* 将分割后的训练集与测试集分配给 `X_train`, `X_test`, `y_train` 和 `y_test`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training and testing split was successful.\n"
     ]
    }
   ],
   "source": [
    "# TODO: Import 'train_test_split'\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# TODO: Shuffle and split the data into training and testing subsets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, prices, test_size=0.2, random_state=5)\n",
    "\n",
    "# Success\n",
    "print(\"Training and testing split was successful.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 3 - 训练及测试\n",
    "*将数据集按一定比例分为训练用的数据集和测试用的数据集对学习算法有什么好处？*\n",
    "\n",
    "*如果用模型已经见过的数据，例如部分训练集数据进行测试，又有什么坏处？*\n",
    "\n",
    "**提示：** 如果没有数据来对模型进行测试，会出现什么问题？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 3 - 回答:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试集可以对模型进行验证、并且评估模型对未知数据是否存在欠拟合和过拟合问题。没有测试集，我们就无法对模型的好坏作出准确的评估。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第四步. 分析模型的表现\n",
    "在项目的第四步，我们来看一下不同参数下，模型在训练集和验证集上的表现。这里，我们专注于一个特定的算法（带剪枝的决策树，但这并不是这个项目的重点），和这个算法的一个参数 `'max_depth'`。用全部训练集训练，选择不同`'max_depth'` 参数，观察这一参数的变化如何影响模型的表现。画出模型的表现来对于分析过程十分有益。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学习曲线\n",
    "下方区域内的代码会输出四幅图像，它们是一个决策树模型在不同最大深度下的表现。每一条曲线都直观得显示了随着训练数据量的增加，模型学习曲线的在训练集评分和验证集评分的变化，评分使用决定系数 $R^2$。曲线的阴影区域代表的是该曲线的不确定性（用标准差衡量）。\n",
    "\n",
    "运行下方区域中的代码，并利用输出的图形回答下面的问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2123861ba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Produce learning curves for varying training set sizes and maximum depths\n",
    "vs.ModelLearning(features, prices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 4 - 学习曲线\n",
    "* 选择上述图像中的其中一个，并给出其最大深度。\n",
    "* 随着训练数据量的增加，训练集曲线的评分有怎样的变化？验证集曲线呢？\n",
    "* 如果有更多的训练数据，是否能有效提升模型的表现呢？\n",
    "\n",
    "**提示：**学习曲线的评分是否最终会收敛到特定的值？一般来说，你拥有的数据越多，模型表现力越好。但是，如果你的训练和测试曲线以高于基准阈值的分数收敛，这是否有必要？基于训练和测试曲线已经收敛的前提下，思考添加更多训练点的优缺点。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 4 - 回答:\n",
    "图 max_depth = 3：\n",
    "- 随着训练数据量的增加，训练集和验证集曲线的评分基本上都趋于平稳，$R^2$ 较高，属于较理想的曲线\n",
    "- 如果有更多得训练数据，训练集和验证集曲线的评分基本上还会处于平稳状态，对于模型训练的提升有限"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 复杂度曲线\n",
    "下列代码内的区域会输出一幅图像，它展示了一个已经经过训练和验证的决策树模型在不同最大深度条件下的表现。这个图形将包含两条曲线，一个是训练集的变化，一个是验证集的变化。跟**学习曲线**相似，阴影区域代表该曲线的不确定性，模型训练和测试部分的评分都用的 `performance_metric` 函数。\n",
    "\n",
    "**运行下方区域中的代码，并利用输出的图形并回答下面的问题5与问题6。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2121ec1208>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs.ModelComplexity(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 5 - 偏差（bias）与方差（variance）之间的权衡取舍\n",
    "* 当模型以最大深度 1训练时，模型的预测是出现很大的偏差还是出现了很大的方差？\n",
    "* 当模型以最大深度10训练时，情形又如何呢？\n",
    "* 图形中的哪些特征能够支持你的结论？\n",
    "  \n",
    "**提示：** 高偏差表示欠拟合（模型过于简单），而高方差表示过拟合（模型过于复杂，以至于无法泛化）。考虑哪种模型（深度1或10）对应着上述的情况，并权衡偏差与方差。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 5 - 回答:\n",
    "- 当模型以最大深度 1 训练时，模型的预测出现了很大的偏差，欠拟合，$R^2$ 分数较低，模型过于简单，训练集和验证集均达不到很高的分数\n",
    "- 当模型以最大深度 10 训练时，模型的预测出现了很大的方差，过拟合，训练集表现好，验证集分数较低，模型泛化能力差"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 6- 最优模型的猜测\n",
    "* 结合问题 5 中的图，你认为最大深度是多少的模型能够最好地对未见过的数据进行预测？\n",
    "* 你得出这个答案的依据是什么？\n",
    "\n",
    "**提示**：查看问题5上方的图表，并查看模型在不同 `depth`下的验证分数。随着深度的增加模型的表现力会变得更好吗？我们在什么情况下获得最佳验证分数而不会使我们的模型过度复杂？请记住，奥卡姆剃刀：“在竞争性假设中，应该选择假设最少的那一个。”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 6 - 回答:\n",
    "- 当模型以最大深度 4 训练时，模型能够最好滴对未见过的数据进行预测\n",
    "- 因为 $R^2$ 的分数在最大深度为 4 时较高，且偏差和方差均较小，训练集和验证集曲线的距离也较近"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第五步. 评估模型的表现\n",
    "在项目的最后一节中，你将构建一个模型，并使用 `fit_model` 中的优化模型去预测客户特征集。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 7- 网格搜索（Grid Search）\n",
    "* 什么是网格搜索法？\n",
    "* 如何用它来优化模型？\n",
    "\n",
    "**提示**：在解释网格搜索算法时，首先要理解我们为什么使用网格搜索算法，以及我们使用它的最终目的是什么。为了使你的回答更具有说服力，你还可以给出一个模型中可以使用此方法进行优化参数的示例。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 7 - 回答:\n",
    "- 网格搜索（Grid Search）是一种通过遍历给定的参数组合来优化模型表现的方法，通过交叉验证(cross-validation)确定最佳效果参数\n",
    "- 网格搜索（Grid Search）只是在将参数组合好了，将数据使用k-fold的方式输入到模型中，然后评估模型的准确性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 8 - 交叉验证\n",
    "- 什么是K折交叉验证法（k-fold cross-validation）？\n",
    "- [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) 是如何结合交叉验证来完成对最佳参数组合的选择的？\n",
    "- [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) 中的`'cv_results_'`属性能告诉我们什么？\n",
    "- 网格搜索为什么要使用K折交叉验证？K折交叉验证能够避免什么问题？\n",
    "\n",
    "**提示**：在解释k-fold交叉验证时，一定要理解'k'是什么，和数据集是如何分成不同的部分来进行训练和测试的，以及基于'k'值运行的次数。\n",
    "在考虑k-fold交叉验证如何帮助网格搜索时，你可以使用特定的数据子集来进行训练与测试有什么缺点，以及K折交叉验证是如何帮助缓解这个问题。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 8 - 回答：\n",
    "- k 折交叉验证法即将数据集分成 k 个子集，每个子集均做一次测试集，其余的作为训练集。如此交叉验证重复 k 次，每次选择一个子集作为测试集，并将 k 次的平均交叉验证识别率作为结果。 \n",
    "- GridSearchCV 传入交叉验证 cv 参数，设置为 k 折交叉验证。对训练集训练完成后调用 bestparams 变量，打印出训练的最佳参数组\n",
    "- 'cv_results_' 保存不同超参数的组合方式，其计算结果以字典的形式保存\n",
    "- 网格搜索算法的本质其实就是穷举，穷举所有的提供的超参的组合情况，并验证模型基于每组参数的表现以达到挑选最优参数组合的目的。网格搜索时使用 k 折交叉验证的优点在于所有的样本都被作为了训练集和测试集，每个样本都被验证了一次，使得模型泛化能力达到最优。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 4：拟合模型\n",
    "在这个练习中，你将需要将所学到的内容整合，使用**决策树算法**训练一个模型。为了得出的是一个最优模型，你需要使用网格搜索法训练模型，以找到最佳的 `'max_depth'` 参数。你可以把`'max_depth'` 参数理解为决策树算法在做出预测前，允许其对数据提出问题的数量。决策树是**监督学习算法**中的一种。\n",
    "\n",
    "另外，你会发现在实现的过程中是使用`ShuffleSplit()`作为交叉验证的另一种形式（参见'cv_sets'变量）。虽然它不是你在问题8中描述的K-fold交叉验证方法，但它同样非常有用！下面的`ShuffleSplit()`实现将创建10个('n_splits')混洗集合，并且对于每个混洗集，数据的20％（'test_size'）将被用作验证集合。当您在实现代码的时候，请思考一下它与`K-fold cross-validation`的不同与相似之处。\n",
    "\n",
    "请注意，`ShuffleSplit` 在 `Scikit-Learn` 版本0.17和0.18中有不同的参数。对于下面代码单元格中的 `fit_model` 函数，您需要实现以下内容：\n",
    "\n",
    "1. **定义 `'regressor'` 变量**: 使用  `sklearn.tree` 中的 [`DecisionTreeRegressor`](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html) 创建一个决策树的回归函数;\n",
    "2. **定义 `'params'` 变量**: 为 `'max_depth'` 参数创造一个字典，它的值是从1至10的数组;\n",
    "3. **定义 `'scoring_fnc'` 变量**: 使用 `sklearn.metrics` 中的 [`make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html)  创建一个评分函数。将 `‘performance_metric’` 作为参数传至这个函数中；\n",
    "4. **定义 `'grid'` 变量**: 使用 `sklearn.model_selection` 中的 [`GridSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) 创建一个网格搜索对象；将变量`'regressor'`, `'params'`, `'scoring_fnc'`和 `'cross_validator'` 作为参数传至这个对象构造函数中；\n",
    "\n",
    "  \n",
    "如果你对 Python 函数的默认参数定义和传递不熟悉，可以参考这个MIT课程的[视频](http://cn-static.udacity.com/mlnd/videos/MIT600XXT114-V004200_DTH.mp4)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Import 'make_scorer', 'DecisionTreeRegressor', and 'GridSearchCV'\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def fit_model(X, y):\n",
    "    \"\"\" Performs grid search over the 'max_depth' parameter for a \n",
    "        decision tree regressor trained on the input data [X, y]. \"\"\"\n",
    "    \n",
    "    # Create cross-validation sets from the training data\n",
    "    # sklearn version 0.18: ShuffleSplit(n_splits=10, test_size=0.1, train_size=None, random_state=None)\n",
    "    # sklearn versiin 0.17: ShuffleSplit(n, n_iter=10, test_size=0.1, train_size=None, random_state=None)\n",
    "    cv_sets = ShuffleSplit(n_splits=10, test_size=0.20, random_state=42)\n",
    "    \n",
    "    # TODO: Create a decision tree regressor object\n",
    "    regressor = DecisionTreeRegressor()\n",
    "\n",
    "    # TODO: Create a dictionary for the parameter 'max_depth' with a range from 1 to 10\n",
    "    params = {'max_depth': range(1,11)}\n",
    "\n",
    "    # TODO: Transform 'performance_metric' into a scoring function using 'make_scorer' \n",
    "    scoring_fnc = make_scorer(performance_metric)\n",
    "\n",
    "    # TODO: Create the grid search cv object --> GridSearchCV()\n",
    "    # Make sure to include the right parameters in the object:\n",
    "    # (estimator, param_grid, scoring, cv) which have values 'regressor', 'params', 'scoring_fnc', and 'cv_sets' respectively.\n",
    "    grid = GridSearchCV(regressor, params, scoring=scoring_fnc, cv=cv_sets)\n",
    "\n",
    "    # Fit the grid search object to the data to compute the optimal model\n",
    "    grid = grid.fit(X, y)\n",
    "\n",
    "    # Return the optimal model after fitting the data\n",
    "    return grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第六步. 做出预测\n",
    "当我们用数据训练出一个模型，它现在就可用于对新的数据进行预测。在决策树回归函数中，模型已经学会对新输入的数据*提问*，并返回对**目标变量**的预测值。你可以用这个预测来获取数据未知目标变量的信息，这些数据必须是不包含在训练数据之内的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 9 - 最优模型\n",
    "*最优模型的最大深度（maximum depth）是多少？此答案与你在**问题 6**所做的猜测是否相同？*\n",
    "\n",
    "运行下方区域内的代码，将决策树回归函数代入训练数据的集合，以得到最优化的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter 'max_depth' is 4 for the optimal model.\n"
     ]
    }
   ],
   "source": [
    "# Fit the training data to the model using grid search\n",
    "reg = fit_model(X_train, y_train)\n",
    "\n",
    "# Produce the value for 'max_depth'\n",
    "print(\"Parameter 'max_depth' is {} for the optimal model.\".format(reg.get_params()['max_depth']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 9 - 回答：\n",
    "最优模型的最大深度（maximum depth）是 4，此答案与在问题 6 所做的猜测是相同的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 10 - 预测销售价格\n",
    "想像你是一个在波士顿地区的房屋经纪人，并期待使用此模型以帮助你的客户评估他们想出售的房屋。你已经从你的三个客户收集到以下的资讯:\n",
    "\n",
    "| 特征 | 客戶 1 | 客戶 2 | 客戶 3 |\n",
    "| :---: | :---: | :---: | :---: |\n",
    "| 房屋内房间总数 | 5 间房间 | 4 间房间 | 8 间房间 |\n",
    "| 社区贫困指数（％被认为是贫困阶层） | 17% | 32% | 3% |\n",
    "| 邻近学校的学生-老师比例 | 15：1 | 22：1 | 12：1 |\n",
    "\n",
    "* 你会建议每位客户的房屋销售的价格为多少？\n",
    "* 从房屋特征的数值判断，这样的价格合理吗？为什么？\n",
    "\n",
    "**提示：**用你在**分析数据**部分计算出来的统计信息来帮助你证明你的答案。\n",
    "\n",
    "运行下列的代码区域，使用你优化的模型来为每位客户的房屋价值做出预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted selling price for Client 1's home: $411,931.58\n",
      "Predicted selling price for Client 2's home: $235,620.00\n",
      "Predicted selling price for Client 3's home: $922,740.00\n"
     ]
    }
   ],
   "source": [
    "# Produce a matrix for client data\n",
    "client_data = [[5, 17, 15], # Client 1\n",
    "               [4, 32, 22], # Client 2\n",
    "               [8, 3, 12]]  # Client 3\n",
    "\n",
    "# Show predictions\n",
    "for i, price in enumerate(reg.predict(client_data)):\n",
    "    print(\"Predicted selling price for Client {}'s home: ${:,.2f}\".format(i+1, price))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 10 - 回答：\n",
    "- 建议客户1的房屋销售价格为：\\$411,931.58，此价格介于客户2和客户3之间，与特征数据吻合，比较合理\n",
    "- 建议客户2的房屋销售价格为：\\$235,620.00，贫困指数最大，学生/老师比例最大，教育投入较少，价格最低合理\n",
    "- 建议客户3的房屋销售价格为：\\$922,740.00，房屋内房间总数最多，建造成本上升，房屋总价越高；贫困指数最小，富人聚集的越多，房价较贫困区也相应增加；根据数据分析：PTRATIO 与 MEDV 相关性不明显，PTRATIO 比例越大，MEDV 在部分区间内下降，由此学生和老师的比例会在一定程度上影响房价，但是不会对房价中位数造成很明显的影响。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 5\n",
    "你刚刚预测了三个客户的房子的售价。在这个练习中，你将用你的最优模型在整个测试数据上进行预测, 并计算相对于目标变量的决定系数 $R^2$ 的值。\n",
    "\n",
    "**提示：**\n",
    "* 你可能需要用到 `X_test`, `y_test`, `optimal_reg`, `performance_metric`。\n",
    "* 参考问题10的代码进行预测。\n",
    "* 参考问题2的代码来计算R^2的值。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal model has R^2 score 0.85 on test data\n"
     ]
    }
   ],
   "source": [
    "# TODO Calculate the r2 score between 'y_true' and 'y_predict'\n",
    "\n",
    "predicted = reg.predict(X_test)\n",
    "r2 = performance_metric(y_test, predicted)\n",
    "\n",
    "print(\"Optimal model has R^2 score {:,.2f} on test data\".format(r2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题11 - 分析决定系数\n",
    "\n",
    "你刚刚计算了最优模型在测试集上的决定系数，你会如何评价这个结果？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题11 - 回答\n",
    "决定系数值为：0.85，预测表现良好，模型准确度扔有待提高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型健壮性\n",
    "\n",
    "一个最优的模型不一定是一个健壮模型。有的时候模型会过于复杂或者过于简单，以致于难以泛化新增添的数据；有的时候模型采用的学习算法并不适用于特定的数据结构；有的时候样本本身可能有太多噪点或样本过少，使得模型无法准确地预测目标变量。这些情况下我们会说模型是欠拟合的。\n",
    "\n",
    "### 问题 12 - 模型健壮性\n",
    "\n",
    "模型是否足够健壮来保证预测的一致性？\n",
    "\n",
    "**提示**: 执行下方区域中的代码，采用不同的训练和测试集执行 `fit_model` 函数10次。注意观察对一个特定的客户来说，预测是如何随训练数据的变化而变化的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 1: $391,183.33\n",
      "Trial 2: $411,417.39\n",
      "Trial 3: $415,800.00\n",
      "Trial 4: $420,622.22\n",
      "Trial 5: $413,334.78\n",
      "Trial 6: $411,931.58\n",
      "Trial 7: $399,663.16\n",
      "Trial 8: $407,232.00\n",
      "Trial 9: $402,531.82\n",
      "Trial 10: $413,700.00\n",
      "\n",
      "Range in prices: $29,438.89\n"
     ]
    }
   ],
   "source": [
    "vs.PredictTrials(features, prices, fit_model, client_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 12 - 回答：\n",
    "\n",
    "- 10 次训练得到的价格范围均在 \\$40,000剩下波动\n",
    "- 价格浮动：\\$29,438.89\n",
    "- 波动还是挺大的，所以模型还不够健壮，仍有优化空间"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 13 - 实用性探讨\n",
    "*简单地讨论一下你建构的模型能否在现实世界中使用？* \n",
    "\n",
    "提示：回答以下几个问题，并给出相应结论的理由：\n",
    "- *1978年所采集的数据，在已考虑通货膨胀的前提下，在今天是否仍然适用？*\n",
    "- *数据中呈现的特征是否足够描述一个房屋？*\n",
    "- *在波士顿这样的大都市采集的数据，能否应用在其它乡镇地区？*\n",
    "- *你觉得仅仅凭房屋所在社区的环境来判断房屋价值合理吗？*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 13 - 回答：\n",
    "- 1978年距离现在时间比较久远，目前购房市场的需求、目的、环境已经不能使用过去的模型来作简单的预估，只能说1978的数据只能作为参考，不一定具有价值\n",
    "- 数据中呈现的特征不能足够描述一个房屋，还要考虑现实中真实的购房目的、房屋所在地的未来发展趋势、潜力、规划等因素\n",
    "- 不能应用在其他乡镇地区，因为大城市与小乡镇的经济结构、人口密度、消费水平、人才水平、教育资源等都不一样\n",
    "- 仅凭房屋所在社区的环境来判断房屋价值不合理，还应该考虑房屋处在社区的位置，开发商品牌实力、物业服务水准、所处楼层、采光度、噪音、垃圾处理站位置等都会影响房屋价格"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第七步.完成和提交"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当你完成了以上所有的代码和问题，你需要将 iPython Notebook 导出 HTML，导出方法：在左上角的菜单中选择 **File -> Download as -> HTML (.html)**。当你提交项目时，需要包含**可运行的 .ipynb 文件**和**导出的 HTML 文件**。"
   ]
  }
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